Contained in:
Book Chapter

A statistical information system in support of job policies orientation

  • Adham Kahlawi
  • Francesca Giambona
  • Lucia Buzzigoli
  • Laura Grassini
  • Cristina Martelli

A significant problem for labour market policies relies on the individuation of the most advisable skills to have and to enhance through focused training offers. Vocational training systems and institutions are called to answer the question posed by every person looking for a new job or professional opportunities: which are the skills-to-have to enhance the professional profile? Many efforts have been made to answer this question, mainly designing predictive models; however, these models are often limited to specific economic sectors and usually don’t adopt a country-specific perspective. This paper proposes a recommendation system oriented to specific users: once that the user has described his/her skills profile, the system suggests the skills that, once got, will fit with the most frequent job vacancies. In this proposal perspective, the skills are proposed regardless of the economic sector, and they are compatible with the characteristics of the specific country labour market. In this contribution, we will focus on the Italian market; the recommendation system is based on the job ads published by Italian companies on various websites for both 2019 and 2020 after the skills required for each job offer have been mapped to one of the skills presented in the classification of European Skills/ competence, qualifications ad Occupations (ESCO).

  • Keywords:
  • job policies,
  • labour market,
  • skills recommender,
  • recommendation system,
+ Show More

Adham Kahlawi

University of Florence, Italy - ORCID: 0000-0003-4040-5590

Francesca Giambona

University of Florence, Italy - ORCID: 0000-0002-1760-2062

Lucia Buzzigoli

University of Florence, Italy - ORCID: 0000-0003-3297-1023

Laura Grassini

University of Florence, Italy - ORCID: 0000-0003-4678-6507

Cristina Martelli

University of Florence, Italy

  1. Al-Otaibi S.T. and Ykhlef M. (2012), A survey of job recommender systems, International Journal of the Physical Sciences, 7(29), pp. 5127-5142.
  2. Chen R., Hua Q., Chang Y-S., Wang B., Zhang L., Kong X. (2018), A Survey of Collaborative Filtering-Based Recommender Systems: From Traditional Methods to Hybrid Methods Based on Social Networks, IEEE, 6, pp.64301-64320.
  3. Giabelli A., Malandri L., Mercorio F., Mezzanzanica M. and Seveso A., (2021), Skills2Job: A recommender system that encodes job offer embeddings on graph databases, Applied Soft Computing Journal 101, pp.107049.
  4. Jariha P. and Jain S.K. (2018), A state-of-the-art Recommender Systems: an overview on Concepts, Methodology and Challenges, Proceedings of the 2nd International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 1769-1774.
  5. Kahlawi A., (2020), An Ontology Driven ESCO LOD Quality Enhancement, International Journal of Advanced Computer Science and Applications (IJACSA), 11(3).
  6. Koren J., Bell R. and Volinsky C. (2009), Matrix Factorization Techniques For Recommender Systems, IEEE COMPUTER, 42(8), pp. 30-37.
  7. Lakshmikanth P, P. Radha K, J. V. R. M, (2021), Approaching the cold-start problem using community detection based alternating least square factorisation in recommendation systems, Evol. Intel. 14, pp. 835–849.
  8. Leskovec J., Rajaraman A., Ullman J. (2019), Mining of Massive Datasets, 3rd Edition, Cambridge University Press.
  9. Martin P M., (2015), Activation and active labour market policies in OECD countries: stylized facts and evidence on their effectiveness, IZA Journal of Labor Policy 4(4).
  10. Mohla D., (2020), IEEE IAS Standards-A Pathway to Narrow the Technical Skills Gap [Standards News],in IEEE Industry Applications Magazine, 26(5), pp. 94-95.
  11. Ras E., Wild F., Stahl C., Baudet A., (2017), Bridging the skills gap of workers in Industry 4.0 by human performance augmentation tools: Challenges and roadmap, Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 428-432.
  12. Tavakoli M., Mol S.T. and Kismih G. (2020), Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners, Proceedings of the 12th International Conference on Computer Supported Education (CSEDU), 2, pp. 96-104.
  13. Valverde-Rebaza J., Puma R., Bustios P. and Silva N.C. (2018), Job Recommendation based on Job Seeker Skills: An Empirical Study, In: A. Jorge, R. Campos, A. Jatowt, S. Nunes (eds.): Proceedings of the Text2StoryIR’18 Workshop, Grenoble, France.
  14. Varanasi B., (2021), Knowledge Obsolescence and the Future of Work: Relevance of Knowledge and Impact to Jobs, Computational Thinking for Problem Solving and Managerial Mindset Training. IGI Global, pp.64-181.
  • Publication Year: 2021
  • Pages: 131-135
  • Content License: CC BY 4.0
  • © 2021 Author(s)

  • Publication Year: 2021
  • Content License: CC BY 4.0
  • © 2021 Author(s)

Chapter Information

Chapter Title

A statistical information system in support of job policies orientation


Adham Kahlawi, Francesca Giambona, Lucia Buzzigoli, Laura Grassini, Cristina Martelli





Peer Reviewed

Publication Year


Copyright Information

© 2021 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

ASA 2021 Statistics and Information Systems for Policy Evaluation

Book Subtitle

BOOK OF SHORT PAPERS of the on-site conference


Bruno Bertaccini, Luigi Fabbris, Alessandra Petrucci

Peer Reviewed

Publication Year


Copyright Information

© 2021 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press



eISBN (pdf)


eISBN (xml)


Series Title

Proceedings e report

Series ISSN


Series E-ISSN






Export Citation


Open Access Books

in the Catalogue


Book Chapters





from 869 Research Institutions

of 64 Nations


scientific boards

from 340 Research Institutions

of 43 Nations



from 345 Research Institutions

of 37 Nations